Noga Zaslavsky

The paper shows that iterated learning leads semantic systems to approach optimal lossy compression of meaning, suggesting a cognitive bias toward efficient representation.

This study shows that high-performing ANNs and brains converge on similar representations, and identifies stimuli that modulate model-to-brain alignment.

The paper shows that language models trained on developmentally realistic data can predict human brain responses to language, achieving near-maximal performance with 100 million words of training.

The paper argues that mapping models in cognitive neuroscience should be chosen based on research goals rather than being strictly linear or nonlinear, and introduces complexity metrics for evaluation.